SegTHOR: Segmentation of Thoracic Organs at Risk in CT images

In the era of open science, public datasets, along with common experimental protocol, help in the process of designing and validating data science algorithms; they also contribute to ease reproductibility and fair comparison between methods. Many datasets for image segmentation are available, each presenting its own challenges; however just a very few exist for radiotherapy planning. This paper is the presentation of a new dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiotherapy. This dataset is called SegTHOR (Segmentation of THoracic Organs at Risk). In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist. Along with the dataset, we present some baseline results, obtained using both the original, state-of-the-art architecture U-Net and a simplified version. We investigate different configurations of this baseline architecture that will serve as comparison for future studies on the SegTHOR dataset. Preliminary results show that room for improvement is left, especially for smallest organs.Comment: Submitted to a journal in december 201

SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDS

International audienc

Інтелектуальна система розпізнавання образів на основі згорткових нейронних мереж

Магістерська дисертація на здобуття ступеня «магістр» за освітньо-науковою програмою підготовки «Інтегровані інформаційні системи» на тему «Інтелектуальна система розпізнавання образів на основі згорткових нейронних мереж». Дисертація містить 102 сторінки, 54 рисунки, 3 додатки, 26 джерел. Актуальність. Підвищення точності розпізнавання графічних образів комп’ютером є актуальною темою для побудови сучасних інформаційних систем. Метою магістерської дисертації є підвищення ефективності систем розпізнавання графічних образів, вдосконалення технології комп’ютерного зору. Об`єкт дослідження: графічний образ. Предмет дослідження: інтелектуальна система розпізнавання графічних образів на основі згорткових нейронних мереж. Наукова новизна полягає у підвищенні ефективності розпізнавання графічних образів інтелектуальними системами, а саме – у поєднанні методів попередньої обробки зображення та мінімізації помилки системи. Публікація результатів дисертації. За результатами роботи було опубліковано наукові статті: Ткаченко М. С., Сокульський О.Є. Застосування R-CNN при автоматичному позиціонуванні об’єктів через нейромережевий аналіз графічних даних. Ткаченко М. С., Сокульський О.Є. Принципи організації процедури машинного аналізу на основі згорткової нейромережевої архітектури.Master's dissertation for the degree of "master" in the educational program "Integrated Information Systems" on the topic "Intelligent image recognition system based on convolutional neural networks." The dissertation contains 102 pages, 54 figures, 3 appendices, 26 sources. Topicality. Improving the accuracy of computer image recognition is an important topic for building modern information systems. The aim is to improve the efficiency of graphic recognition systems, and enhance computer vision technology. The object of study - graphic image. Purpose of the study - intelligent graphic image recognition system based on convolutional neural networks. Scientific novelty is to increase the efficiency of graphic image recognition by intelligent systems, namely - in a combination of image pre-processing methods and minimize system error Publication of dissertation results. Based on the results of the work, an articles were published: Tkachenko M. Sokylskyi O. Usage of R-CNN in automatic positioning of objects through neural network analysis of graphic data. Tkachenko M. Sokylskyi O. Principles of organization of machine analysis procedure based on convolutional neural network architecture

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity